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   Copyright (C) 2005 Jean-Baptiste Veyrieras, INRA.

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File: metaqtl-ref.info,  Node: Top,  Next: Introduction,  Up: (dir)

MetaQTL
*******

Copyright (C) 2005 Jean-Baptiste Veyrieras, INRA.

     Permission is granted to copy, distribute and/or modify this
     document under the terms of the GNU Free Documentation License,
     Version 1.2 or any later version published by the Free Software
     Foundation; with the Invariant Sections being "GNU General Public
     License" and "Free Software Needs Free Documentation", the
     Front-Cover text being "A GNU Manual", and with the Back-Cover
     Text being (a) (see below).  A copy of the license is included in
     the section entitled "GNU Free Documentation License".

     (a) The Back-Cover Text is: "You have freedom to copy and modify
     this GNU Manual, like GNU software. Copies published by the Free
     Software Foundation raise funds for GNU development."

* Menu:

* Introduction::
* Data Base::
* Meta Analysis::
* Visualization::
* Utilities::
* Tutorial::
* Copying This Manual::


File: metaqtl-ref.info,  Node: Introduction,  Next: Data Base,  Prev: Top,  Up: Top

1 Introduction
**************

* Menu:

* General Overview::
* Citing MetaQTL::
* How to Get and Install MetaQTL::
* Getting Help::
* General Usage of the Programs::
* References::


File: metaqtl-ref.info,  Node: General Overview,  Next: Citing MetaQTL,  Up: Introduction

1.1 General Overview
====================

* Menu:

* What is MetaQTL::
* Definition of the Problem::


File: metaqtl-ref.info,  Node: What is MetaQTL,  Next: Definition of the Problem,  Up: General Overview

1.1.1 What is MetaQTL
---------------------

MetaQTL is a suite of programs designed to carry out meta-analysis of
QTL mapping experiments. A QTL mapping experiment consists in a genetic
linkage map and a set of quantitative trait loci (QTL) which have been
detected and positioned onto the genetic linkage map. This package is
composed by several programs written in pure Java. This programs can
perform various tasks, including reformatting, analyzing data and
visualizing the results of the analyses. Presently the programs can
handle data from backcross, intercrosses and recombinant inbreds, as
well as a few other experimental designs. This project is ongoing and
suggestions are welcome for further improvments and enhancements.


File: metaqtl-ref.info,  Node: Definition of the Problem,  Prev: What is MetaQTL,  Up: General Overview

1.1.2 Definition of the Problem
-------------------------------

Since the last decade, the advent of molecular markers have accelerated
the pace of discovering the loci which are implied in quantitative
trait variations. Quantitative Trait Loci (QTL) mapping usually begins
with the collection of genotypic (based on molecular markers) and
phenotypic data from a segregating population. First, from the
genoptypic data the markers are both ordered and positioned on a
genetic map using standard linkage mapping approaches. Secondly,
refinement of analytical methods have enabled to detect one ore several
QTL on each chromosome (see for instance Lander and Botstein 1989, Zeng
1994). Nevertheless due to the limiting number of individuals and
generations in usual experiment this approach generally leads to QTL
locations with a confidence interval (CI) around 10 cM (Kearsey 1998):
e.g in plant genomes this corresponds to thousand of genes.

   Due to its relative simplicity and its compelling concept QTL
mapping has been widely used and more and more QTL detection results
are now available in public databases (e.g in maize at
`http://www.maizegdb.org'). One of the main purpose of these databases
was to facilitate the comparison of different QTL detection results by
providing both standard description of these results and ontologies
(see for instance the trait ontology at
`http://www.gramene.org/plant\_ontology/'). Relevance of comparative
analysis of QTL studies have been illustrated by several authors
(Khavkin 1997 and 1998, Lin 1995). However this studies often relied on
simple descriptive statistics.

   This gap in QTL congruency study was partially bridged by Goffinet
and Gerber (2000) who proposed a meta-analysis based approach to
combine several QTL results. Their method makes it possible to evaluate
how many "actual" QTL locations underly the distribution of the
observed QTL on the genome. This approach has been implemented in
BioMercator by Arcade et al. (2004). This software allows user to merge
both markers and QTL onto a consensus map by means of an iterative
projection procedure. Then the algorithm devised by Goffinet and Gerber
(2000) can be applied to evaluate the likelihood of clustering the
observed QTL in 1,2,3 or 4 groups. Afterward, the optimal number of
clusters is seleced by using a Akaike like criterion. Alghough original
this approach suffers from the absence of indicator to assess the
consensus map quality and from the limiting number of QTL clusters
which can be explored.

   Based on recent methodological developments, MetaQTL implements a
series of Java programs in order to carry out more sophisticated QTL
meta-analysis. All the programs in MetaQTL are command line programs.
Each program does a small job and the user can combine the program as a
group to do a complete analysis.


File: metaqtl-ref.info,  Node: Citing MetaQTL,  Next: How to Get and Install MetaQTL,  Prev: General Overview,  Up: Introduction

1.2 Citing MetaQTL
==================

In publications you should cite this manual.

   * Veyrieras, 2005. MetaQTL, Version 1.0. INRA, France.


File: metaqtl-ref.info,  Node: How to Get and Install MetaQTL,  Next: Getting Help,  Prev: Citing MetaQTL,  Up: Introduction

1.3 How to Get and Install MetaQTL
==================================

MetaQTL is avalaible at `http://www.bioinformatics/mqtl'. As MetaQTL is
entirely written in Java (1.4) it should be run on most of operating
systems provided that a compatible Java Virtual Machine (1.4 or higher)
has been previously installed on your machine. Otherwise go to
`http://java.sun.com/downloads/index.html': then download and install
the last version of the Java 2 Platform.  To run the programs of
MetaQTL you must have updated the Java CLASSPATH  by adding the path to
the jar archive `metaqtl.jar' included into the MetaQTL distribution.
Under UNIX like operating system this can be done as follows

     bash$ export CLASSPATH=${CLASSPATH}:/path/to/metaqtl/directory/metaqtl.jar
Under Windows 9x/NT/2000/XP you can look at
`http://support.microsoft.com/' to find how to update or create the
environment variable CLASSPATH. Generally, to view or change
environment variables you have to apply the following steps:
   * Right-click My Computer, and then click Properties.

   * Click the Advanced tab.

   * Click Environment variables.

   * Click one the following options, for either a user or a system
     variable:

   * Click New to add a new variable name and value.

   * Click an existing variable, and then click Edit to change its name
     or value (here the value will be the entire file path from the
     root of the disk to the jar archive of MetaQTL).
   In the next sections, the examples are given assuming the CLASSPATH
have been correctly set.


File: metaqtl-ref.info,  Node: Getting Help,  Next: General Usage of the Programs,  Prev: How to Get and Install MetaQTL,  Up: Introduction

1.4 Getting Help
================

For any questions or troubles about the use of MetaQTL, contact J.B.
Veyrieras by email at <veyrieras@moulon.inra.fr>.


File: metaqtl-ref.info,  Node: General Usage of the Programs,  Next: References,  Prev: Getting Help,  Up: Introduction

1.5 General Usage of the Programs
=================================

The programs in the MetaQTL suite all have the same look and feel and
are heavily influenced by UNIX programs. They can only be used as
command line programs. The options `-v, --verbose' and `-h, --help' are
common to all the programs. The first one activates the verbosity mode
and the second one can be used to display a brief help on program usage.


File: metaqtl-ref.info,  Node: References,  Prev: General Usage of the Programs,  Up: Introduction

1.6 References
==============

   * A. Arcade, A. Labourdette, M. Falque, B. Mangin, F. Chardon et
     al., 2004, BioMercator: integrating genetic maps and QTL towards
     discovery of candidate genes, Bioinformatics, 20:2324-2326.

   * A.P. Dempster, N.M. Laird, D.B. Rubin, 1977, Maximum Likelihood
     from incomplete data via the EM algorithm (with  discussion), J.
     Roy Stat. Soc., B 39:1-38.

   * B. Goffinet and S. Gerber., 2000, Quantitative trait loci: a
     meta-analysis, Genetics, 155(1):463-473.

   * M.J. Kearsey and A.G.L. Farquhar., 1998, QTL analysis in plants;
     where are we now?, Heredity, 80:137-142.

   * E. Khavkin and E. H. Coe., 1997, Mapped genomic locations for
     developmental functions and QTLs reflect concerned groups in maize
     (Zea mays L.), Theor. Appl. Genet., 95:343-352.

   * E. Khavkin and E. H. Coe., 1998, The major quantitative trait loci
     for plant stature, development and yield are general
     manifestations of developmental gene clusters, Maize Newslett.,
     72:60-66.

   * E. S.Lander and D. Botstein, 1989, Mapping Mendelien factors
     underlying quantitative traits using RFLP linkage maps, Genetics,
     121:185-199.

   * Y. R. Lin, K. F. Schertz, A. H. Paterson, 1995, Comparative
     analysis of QTLS affecting plant height and maturity across the
     poaceae, in reference to an interspecific sorghum population,
     Genetics, 141:391-411.

   * T. Sasaki, T. Matsumoto, K. Yamamoto, K. Sakata, 2002, et al., The
     genome sequence and structure of rice chromosome 1, Nature,
     420(6913):312-316.

   * J-B. Veyrieras, B. Goffinet, A. Charcosset, 2005, Meta-analysis of
     QTL Mapping Experiments, in prep., 2005.

   * J. H. Ward., 1963, Hierarchical Grouping to Optimize an Objective
     Function, J. Am. Stat. Assoc., 58:236-244.

   * Z-B. Zeng, 1994, Precision mapping of quantitative trait loci,
     Genetics, 136(4):1457-1468.


File: metaqtl-ref.info,  Node: Data Base,  Next: Meta Analysis,  Prev: Introduction,  Up: Top

2 Data Base
***********

* Menu:

* Overview::
* MetaDB::


File: metaqtl-ref.info,  Node: Overview,  Next: MetaDB,  Up: Data Base

2.1 Overview
============

The first phase in using MetaQTL is to create the database to store the
QTL mapping experiments. The database here relies on a simple multiple
file framework. All the tables of the database are plain text file
where the first line contains the labels of the columns and the
following lines represents the records. For both the header and the
records the fields are separated by a TABULATION. The global database
(METAdb) consists of 3 tables, the experiment table, the trait ontology
table and the marker dictionary table, plus 2 sub databases, the
genetic linkage map database (GLMdb) and the QTL database (QTLdb). In
the next sections we provide a full description of the tables of the
database. We have adopted the following convention to specify each
field of the tables: the fields in bold are required, i.e they must be
specified in the header of the file meanwhile the other ones can be
ignored. For each field we have indicated the type of the value between
brackets (e.g STRING, INTEGER, BOOLEAN,...).

* Menu:

* Experiment Table::
* Trait Ontology Table::
* GLMdb::
* QTLdb::


File: metaqtl-ref.info,  Node: Experiment Table,  Next: Trait Ontology Table,  Up: Overview

2.1.1 Experiment Table
----------------------

The experiment table summarizes the parameters of each mapping
experiment. This table contains the following fields:

   * map (STRING) : the name of the mapping experiment which must be
     unique.

   * mapping.function (STRING) : the name of the mapping function used
     to build the genetic map for this mapping experiment. Presently
     the possible values are:
        - haldane

        - kosambi

   * mapping.unit (STRING) : the unit of the genetic distances of the
     genetic linkage map. Possible values are M for Morgan or cM for
     centiMorgan.

   * mapping expansion (BOOLEAN) : 1 if the genetic linkage map of this
     mapping experiment must be rescaled.

   * mapping.cross : the cross design used to build the map (may differ
     from the cross.type).

   * cross.type (STRING) : the cross design used to build the genetic
     linkage map. MetaQTL is able to deal with a various type of
     experimental design. The design is encoding by a string of
     characters. For example BC stands for backcross, SFi stands for
     selfed intercross lines and the integer i indicates the generation
     (if i=2 then we have a classical F2). All of the supported
     experimental designs are listed in the table below.

